SASM: A Tool for Sentiment Analysis on Twitter

With Twitter ranking as one of the fastest growing social media platform, it represents a means via which simultaneous sharing of opinion is made possible. This huge resources for information is however limited in its ability to present human readers and opinion seekers relevant information tailored towards experience, ability to extract, read, summarize and finally organize them in appropriately usable forms. The volume of available tweets is not actually the problem, but the nature of the data which harbors a lot of sentiment. This paper is set to present improved means of accurately providing analysis of automatically retrieved opinions and presenting the results to the user after performing sentiment analysis on the retrieved data.